Spoken language systems such as speech-to-speech dialog translation systems have been gaining more attention in recent years. These systems require full integration of speech recognition and natural language understanding. This paper presents an efficient parsing algorithm that integrates the search problems of speech processing and language processing. The parsing algorithm we propose here is regarded as an extension of the finite-state-network directed, one-pass search algorithm to one directed by a context-free grammar with retention of the time-synchronous procedure. The extended search algorithm is used to find approximately globally optimal sentence hypotheses; it does not have overhead which exists in, for example, hierarchical systems based on the lattice parsing approach. The computational complexity of this search algorithm is proportional to the length of the input speech. As the search process in the speech recognition can directly take account of the predictive information in the sentence parsing, this framework can be extended to sopken language systems which deal with dynamically varying constraints in dialogue situations.